Galapagos 101: Fundamentals of Grasshopper's Evolutionary Solver for Architectural Design
Simulation and Analysis
Harness the capabilities of evolutionary computing in our Galapagos Solver course. Master techniques for intricate design optimization and apply them in real-world architectural projects.
Arie-Willem de Jongh
About this course
In this 101 series about evolutionary computing or evolutionary solving, I’m going to go over the basics of evolutionary solving and how and why it can be useful for you in everyday architecture practice.
This plugin comes standard within Grasshopper and is very helpful to optimize complex design questions with lots of variables. A couple of examples would be; optimizing a building mass to maximize its views, optimizing a facade system according to solar radiation or use it to optimize member sizes in a structural space frame to reduce weight and cost.
We’re going to start with a basic example where we model a random landscape with peaks and valleys and let Galapagos find the highest peak. This example is perfect for you to familiarize with the concept and the workings of an evolutionary solver.
After this, we’ll take a look at a simple real-life scenario where we’ll optimize a set of plot outlines by equalizing their floor areas. We’ll end with adding, normalizing and weighting multiple fitness values into Galapagos. In this case, floor areas and the shape of the plot outlines.
1.- Introduction and brief history of evolutionary solving
02min 00seg2.- Setting up our landscape
12min 17seg3.- Running the Galapagos solver
13min 14seg4.- Optimizing square meters of a building lot
11min 59seg5.- Using the Galapagos solver to equalize the individual lots
14min 01seg6.- Optimizing multiple fitness values
17min 35seg